Overview

Dataset statistics

Number of variables16
Number of observations4315
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory539.5 KiB
Average record size in memory128.0 B

Variable types

Numeric15
Categorical1

Alerts

recency is highly correlated with num_purchases and 1 other fieldsHigh correlation
avg_days_bw_purchases is highly correlated with num_purchasesHigh correlation
num_purchases is highly correlated with recency and 4 other fieldsHigh correlation
frequency is highly correlated with recency and 1 other fieldsHigh correlation
revenue is highly correlated with num_purchases and 3 other fieldsHigh correlation
avg_ticket is highly correlated with revenue and 2 other fieldsHigh correlation
qty_items is highly correlated with num_purchases and 3 other fieldsHigh correlation
avg_basket_size is highly correlated with revenue and 2 other fieldsHigh correlation
returns_revenue is highly correlated with avg_return_revenue and 2 other fieldsHigh correlation
avg_return_revenue is highly correlated with returns_revenue and 2 other fieldsHigh correlation
num_returns is highly correlated with returns_revenue and 2 other fieldsHigh correlation
qty_returned is highly correlated with returns_revenue and 2 other fieldsHigh correlation
num_purchases is highly correlated with revenue and 2 other fieldsHigh correlation
revenue is highly correlated with num_purchases and 1 other fieldsHigh correlation
avg_ticket is highly correlated with avg_basket_sizeHigh correlation
qty_items is highly correlated with num_purchases and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_ticketHigh correlation
returns_revenue is highly correlated with avg_return_revenue and 1 other fieldsHigh correlation
avg_return_revenue is highly correlated with returns_revenue and 1 other fieldsHigh correlation
num_returns is highly correlated with num_purchasesHigh correlation
qty_returned is highly correlated with returns_revenue and 1 other fieldsHigh correlation
num_purchases is highly correlated with revenue and 1 other fieldsHigh correlation
revenue is highly correlated with num_purchases and 2 other fieldsHigh correlation
avg_ticket is highly correlated with revenue and 1 other fieldsHigh correlation
qty_items is highly correlated with num_purchases and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_ticketHigh correlation
returns_revenue is highly correlated with avg_return_revenue and 2 other fieldsHigh correlation
avg_return_revenue is highly correlated with returns_revenue and 2 other fieldsHigh correlation
num_returns is highly correlated with returns_revenue and 2 other fieldsHigh correlation
qty_returned is highly correlated with returns_revenue and 2 other fieldsHigh correlation
customer_id is highly correlated with countryHigh correlation
country is highly correlated with customer_id and 4 other fieldsHigh correlation
recency is highly correlated with date_rangeHigh correlation
avg_days_bw_purchases is highly correlated with date_rangeHigh correlation
num_purchases is highly correlated with revenue and 2 other fieldsHigh correlation
date_range is highly correlated with recency and 1 other fieldsHigh correlation
revenue is highly correlated with country and 5 other fieldsHigh correlation
avg_ticket is highly correlated with revenue and 3 other fieldsHigh correlation
qty_items is highly correlated with country and 2 other fieldsHigh correlation
avg_basket_size is highly correlated with country and 2 other fieldsHigh correlation
returns_revenue is highly correlated with avg_ticket and 1 other fieldsHigh correlation
num_returns is highly correlated with country and 2 other fieldsHigh correlation
qty_returned is highly correlated with avg_ticket and 1 other fieldsHigh correlation
frequency is highly skewed (γ1 = 58.77467773) Skewed
revenue is highly skewed (γ1 = 21.4956196) Skewed
qty_items is highly skewed (γ1 = 22.96127331) Skewed
returns_revenue is highly skewed (γ1 = -62.33766618) Skewed
avg_return_revenue is highly skewed (γ1 = -65.58048429) Skewed
qty_returned is highly skewed (γ1 = -61.38913163) Skewed
customer_id has unique values Unique
avg_days_bw_purchases has 1545 (35.8%) zeros Zeros
returns_revenue has 2824 (65.4%) zeros Zeros
avg_return_revenue has 2824 (65.4%) zeros Zeros
num_returns has 2824 (65.4%) zeros Zeros
qty_returned has 2824 (65.4%) zeros Zeros

Reproduction

Analysis started2022-03-02 13:57:33.727627
Analysis finished2022-03-02 13:59:06.392451
Duration1 minute and 32.66 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct4315
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15302.09316
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-03-02T10:59:06.753213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12618.7
Q113816.5
median15301
Q316779.5
95-th percentile17981.2
Maximum18287
Range5940
Interquartile range (IQR)2963

Descriptive statistics

Standard deviation1720.047195
Coefficient of variation (CV)0.1124060072
Kurtosis-1.195069703
Mean15302.09316
Median Absolute Deviation (MAD)1481
Skewness0.0006870301126
Sum66028532
Variance2958562.353
MonotonicityNot monotonic
2022-03-02T10:59:07.178346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
152801
 
< 0.1%
157001
 
< 0.1%
172991
 
< 0.1%
128371
 
< 0.1%
150761
 
< 0.1%
174441
 
< 0.1%
159211
 
< 0.1%
157471
 
< 0.1%
158401
 
< 0.1%
Other values (4305)4305
99.8%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123491
< 0.1%
123501
< 0.1%
123521
< 0.1%
123531
< 0.1%
123541
< 0.1%
123551
< 0.1%
123561
< 0.1%
123571
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182811
< 0.1%
182801
< 0.1%
182781
< 0.1%
182771
< 0.1%
182761
< 0.1%
182731
< 0.1%
182721
< 0.1%

country
Categorical

HIGH CORRELATION

Distinct35
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size33.8 KiB
United Kingdom
3905 
Germany
 
94
France
 
87
Spain
 
27
Belgium
 
24
Other values (30)
 
178

Length

Max length20
Median length14
Mean length13.33812283
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.2%

Sample

1st rowUnited Kingdom
2nd rowUnited Kingdom
3rd rowFrance
4th rowUnited Kingdom
5th rowUnited Kingdom

Common Values

ValueCountFrequency (%)
United Kingdom3905
90.5%
Germany94
 
2.2%
France87
 
2.0%
Spain27
 
0.6%
Belgium24
 
0.6%
Switzerland20
 
0.5%
Portugal19
 
0.4%
Italy14
 
0.3%
Finland12
 
0.3%
Norway10
 
0.2%
Other values (25)103
 
2.4%

Length

2022-03-02T10:59:07.681146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united3907
47.4%
kingdom3905
47.4%
germany94
 
1.1%
france87
 
1.1%
spain27
 
0.3%
belgium24
 
0.3%
switzerland20
 
0.2%
portugal19
 
0.2%
italy14
 
0.2%
finland12
 
0.1%
Other values (30)126
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

recency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct304
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.02502897
Minimum1
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-03-02T10:59:08.327429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q118
median51
Q3143
95-th percentile312
Maximum374
Range373
Interquartile range (IQR)125

Descriptive statistics

Standard deviation100.1640626
Coefficient of variation (CV)1.076743148
Kurtosis0.4282660467
Mean93.02502897
Median Absolute Deviation (MAD)40
Skewness1.246769578
Sum401403
Variance10032.83943
MonotonicityNot monotonic
2022-03-02T10:59:08.794667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2103
 
2.4%
594
 
2.2%
494
 
2.2%
389
 
2.1%
979
 
1.8%
1177
 
1.8%
1874
 
1.7%
871
 
1.6%
1070
 
1.6%
1664
 
1.5%
Other values (294)3500
81.1%
ValueCountFrequency (%)
135
 
0.8%
2103
2.4%
389
2.1%
494
2.2%
594
2.2%
648
1.1%
871
1.6%
979
1.8%
1070
1.6%
1177
1.8%
ValueCountFrequency (%)
37417
0.4%
37317
0.4%
3726
 
0.1%
3703
 
0.1%
3695
 
0.1%
3685
 
0.1%
36710
0.2%
36610
0.2%
3656
 
0.1%
3636
 
0.1%

avg_days_bw_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1155
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.60680275
Minimum0
Maximum366
Zeros1545
Zeros (%)35.8%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-03-02T10:59:09.239591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median31.18181818
Q373.29166667
95-th percentile184
Maximum366
Range366
Interquartile range (IQR)73.29166667

Descriptive statistics

Standard deviation65.35409852
Coefficient of variation (CV)1.291409355
Kurtosis4.635223005
Mean50.60680275
Median Absolute Deviation (MAD)31.18181818
Skewness1.986172394
Sum218368.3539
Variance4271.158193
MonotonicityNot monotonic
2022-03-02T10:59:09.661298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01545
35.8%
7021
 
0.5%
4618
 
0.4%
5517
 
0.4%
4916
 
0.4%
9116
 
0.4%
2115
 
0.3%
3515
 
0.3%
3115
 
0.3%
4215
 
0.3%
Other values (1145)2622
60.8%
ValueCountFrequency (%)
01545
35.8%
19
 
0.2%
24
 
0.1%
2.8615384621
 
< 0.1%
36
 
0.1%
3.3303571431
 
< 0.1%
3.3513513511
 
< 0.1%
44
 
0.1%
4.1910112361
 
< 0.1%
4.2758620691
 
< 0.1%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3641
 
< 0.1%
3631
 
< 0.1%
3572
< 0.1%
3561
 
< 0.1%
3552
< 0.1%
3521
 
< 0.1%
3512
< 0.1%
3503
0.1%

num_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.259559676
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-03-02T10:59:10.099417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile13
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.659012726
Coefficient of variation (CV)1.798076165
Kurtosis244.1691457
Mean4.259559676
Median Absolute Deviation (MAD)1
Skewness11.95400266
Sum18380
Variance58.66047593
MonotonicityNot monotonic
2022-03-02T10:59:10.545374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11493
34.6%
2825
19.1%
3501
 
11.6%
4394
 
9.1%
5237
 
5.5%
6173
 
4.0%
7139
 
3.2%
898
 
2.3%
968
 
1.6%
1055
 
1.3%
Other values (46)332
 
7.7%
ValueCountFrequency (%)
11493
34.6%
2825
19.1%
3501
 
11.6%
4394
 
9.1%
5237
 
5.5%
6173
 
4.0%
7139
 
3.2%
898
 
2.3%
968
 
1.6%
1055
 
1.3%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
911
< 0.1%
901
< 0.1%
861
< 0.1%
731
< 0.1%
622
< 0.1%
601
< 0.1%

date_range
Real number (ℝ≥0)

HIGH CORRELATION

Distinct374
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.226883
Minimum1
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-03-02T10:59:10.985152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19
Q175.5
median191
Q3286
95-th percentile363
Maximum374
Range373
Interquartile range (IQR)210.5

Descriptive statistics

Standard deviation114.9992631
Coefficient of variation (CV)0.6142240971
Kurtosis-1.330278472
Mean187.226883
Median Absolute Deviation (MAD)106
Skewness0.01411592571
Sum807884
Variance13224.83052
MonotonicityNot monotonic
2022-03-02T10:59:11.453429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36630
 
0.7%
36428
 
0.6%
2526
 
0.6%
6526
 
0.6%
35026
 
0.6%
26725
 
0.6%
35725
 
0.6%
36524
 
0.6%
5424
 
0.6%
2023
 
0.5%
Other values (364)4058
94.0%
ValueCountFrequency (%)
110
0.2%
28
0.2%
310
0.2%
412
0.3%
510
0.2%
67
0.2%
77
0.2%
811
0.3%
96
0.1%
108
0.2%
ValueCountFrequency (%)
37417
0.4%
37321
0.5%
37214
0.3%
3718
 
0.2%
37011
 
0.3%
36911
 
0.3%
36815
0.3%
36720
0.5%
36630
0.7%
36524
0.6%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1403
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04748807193
Minimum0.002673796791
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-03-02T10:59:11.918694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.002673796791
5-th percentile0.003286238136
Q10.01025641026
median0.0192926045
Q30.03566129716
95-th percentile0.1017323586
Maximum34
Range33.9973262
Interquartile range (IQR)0.0254048869

Descriptive statistics

Standard deviation0.5380293109
Coefficient of variation (CV)11.32977796
Kurtosis3682.62375
Mean0.04748807193
Median Absolute Deviation (MAD)0.01151050333
Skewness58.77467773
Sum204.9110304
Variance0.2894755394
MonotonicityNot monotonic
2022-03-02T10:59:12.421905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0185185185227
 
0.6%
0.0384615384627
 
0.6%
0.0322580645223
 
0.5%
0.0196078431423
 
0.5%
0.0153846153823
 
0.5%
0.0212765957422
 
0.5%
0.0526315789522
 
0.5%
0.0192307692321
 
0.5%
0.0163934426221
 
0.5%
0.02520
 
0.5%
Other values (1393)4086
94.7%
ValueCountFrequency (%)
0.00267379679116
0.4%
0.00268096514716
0.4%
0.0026881720436
 
0.1%
0.0027027027032
 
< 0.1%
0.00271002715
 
0.1%
0.0027173913045
 
0.1%
0.002724795649
0.2%
0.00273224043710
0.2%
0.0027397260276
 
0.1%
0.0027548209376
 
0.1%
ValueCountFrequency (%)
341
 
< 0.1%
61
 
< 0.1%
41
 
< 0.1%
26
0.1%
1.51
 
< 0.1%
1.3333333332
 
< 0.1%
16
0.1%
0.66666666673
0.1%
0.55227882041
 
< 0.1%
0.53494623661
 
< 0.1%

revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct4229
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1922.049105
Minimum2.9
Maximum278778.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-03-02T10:59:12.867285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile110.785
Q1300.675
median654.92
Q31611.86
95-th percentile5659.096
Maximum278778.02
Range278775.12
Interquartile range (IQR)1311.185

Descriptive statistics

Standard deviation8325.527494
Coefficient of variation (CV)4.331589381
Kurtosis595.0826196
Mean1922.049105
Median Absolute Deviation (MAD)454.2
Skewness21.4956196
Sum8293641.89
Variance69314408.06
MonotonicityNot monotonic
2022-03-02T10:59:13.297711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.324
 
0.1%
35.43
 
0.1%
153
 
0.1%
4403
 
0.1%
363.653
 
0.1%
79.23
 
0.1%
113.53
 
0.1%
127.862
 
< 0.1%
598.22
 
< 0.1%
1442
 
< 0.1%
Other values (4219)4287
99.4%
ValueCountFrequency (%)
2.91
 
< 0.1%
3.751
 
< 0.1%
5.91
 
< 0.1%
12.241
 
< 0.1%
12.751
 
< 0.1%
153
0.1%
171
 
< 0.1%
20.82
< 0.1%
25.51
 
< 0.1%
301
 
< 0.1%
ValueCountFrequency (%)
278778.021
< 0.1%
259657.31
< 0.1%
189735.531
< 0.1%
133007.131
< 0.1%
123638.181
< 0.1%
114505.321
< 0.1%
88138.21
< 0.1%
65920.121
< 0.1%
62924.11
< 0.1%
59419.341
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4223
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean371.1365673
Minimum1.45
Maximum13206.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-03-02T10:59:13.735574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile88.0344
Q1174.385
median283.2675
Q3421.7729412
95-th percentile892.395
Maximum13206.5
Range13205.05
Interquartile range (IQR)247.3879412

Descriptive statistics

Standard deviation465.0749814
Coefficient of variation (CV)1.253110101
Kurtosis202.5706982
Mean371.1365673
Median Absolute Deviation (MAD)118.1975
Skewness10.64623904
Sum1601454.288
Variance216294.7384
MonotonicityNot monotonic
2022-03-02T10:59:14.217962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.324
 
0.1%
1203
 
0.1%
4403
 
0.1%
79.23
 
0.1%
113.53
 
0.1%
35.43
 
0.1%
90.62
 
< 0.1%
153.2752
 
< 0.1%
172.252
 
< 0.1%
151.052
 
< 0.1%
Other values (4213)4288
99.4%
ValueCountFrequency (%)
1.451
< 0.1%
3.751
< 0.1%
5.91
< 0.1%
7.51
< 0.1%
9.141
< 0.1%
11.671
< 0.1%
12.241
< 0.1%
12.751
< 0.1%
152
< 0.1%
171
< 0.1%
ValueCountFrequency (%)
13206.51
< 0.1%
9338.381
< 0.1%
7178.6333331
< 0.1%
6207.671
< 0.1%
6181.9091
< 0.1%
4873.811
< 0.1%
4366.781
< 0.1%
4327.6216671
< 0.1%
4314.721
< 0.1%
4151.261
< 0.1%

qty_items
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1767
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1132.986095
Minimum1
Maximum197132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-03-02T10:59:14.748013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile46
Q1159
median373
Q3980.5
95-th percentile3503.4
Maximum197132
Range197131
Interquartile range (IQR)821.5

Descriptive statistics

Standard deviation4700.732638
Coefficient of variation (CV)4.14897646
Kurtosis778.4817433
Mean1132.986095
Median Absolute Deviation (MAD)273
Skewness22.96127331
Sum4888835
Variance22096887.33
MonotonicityNot monotonic
2022-03-02T10:59:15.265427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8819
 
0.4%
12018
 
0.4%
7216
 
0.4%
10615
 
0.3%
12815
 
0.3%
15014
 
0.3%
7814
 
0.3%
14614
 
0.3%
18713
 
0.3%
16013
 
0.3%
Other values (1757)4164
96.5%
ValueCountFrequency (%)
12
 
< 0.1%
26
0.1%
33
0.1%
47
0.2%
53
0.1%
63
0.1%
71
 
< 0.1%
81
 
< 0.1%
92
 
< 0.1%
105
0.1%
ValueCountFrequency (%)
1971321
< 0.1%
772421
< 0.1%
771811
< 0.1%
690411
< 0.1%
641241
< 0.1%
630141
< 0.1%
618081
< 0.1%
580211
< 0.1%
570261
< 0.1%
493911
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2244
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.1271216
Minimum0.25
Maximum7824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-03-02T10:59:15.907246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile30
Q180
median140
Q3236.2055556
95-th percentile524.1
Maximum7824
Range7823.75
Interquartile range (IQR)156.2055556

Descriptive statistics

Standard deviation269.8954076
Coefficient of variation (CV)1.348619844
Kurtosis193.3983166
Mean200.1271216
Median Absolute Deviation (MAD)70.63636364
Skewness10.01419872
Sum863548.5298
Variance72843.53103
MonotonicityNot monotonic
2022-03-02T10:59:16.376149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12019
 
0.4%
7218
 
0.4%
6417
 
0.4%
4416
 
0.4%
13616
 
0.4%
14416
 
0.4%
10015
 
0.3%
14615
 
0.3%
10615
 
0.3%
6015
 
0.3%
Other values (2234)4153
96.2%
ValueCountFrequency (%)
0.251
 
< 0.1%
0.66666666671
 
< 0.1%
12
 
< 0.1%
24
0.1%
34
0.1%
3.3333333331
 
< 0.1%
47
0.2%
53
0.1%
5.251
 
< 0.1%
5.51
 
< 0.1%
ValueCountFrequency (%)
78241
< 0.1%
43001
< 0.1%
42801
< 0.1%
3218.4166671
< 0.1%
30281
< 0.1%
29241
< 0.1%
28801
< 0.1%
27081
< 0.1%
2663.9459461
< 0.1%
25291
< 0.1%

avg_unique_prods
Real number (ℝ≥0)

Distinct1001
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.64341438
Minimum1
Maximum297.8823529
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-03-02T10:59:17.526072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.917819149
Q19.3875
median17
Q327.75
95-th percentile56
Maximum297.8823529
Range296.8823529
Interquartile range (IQR)18.3625

Descriptive statistics

Standard deviation19.4417659
Coefficient of variation (CV)0.8982762867
Kurtosis23.96848672
Mean21.64341438
Median Absolute Deviation (MAD)8.5
Skewness3.301064514
Sum93391.33303
Variance377.9822612
MonotonicityNot monotonic
2022-03-02T10:59:17.913605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1398
 
2.3%
198
 
2.3%
1087
 
2.0%
981
 
1.9%
1180
 
1.9%
1474
 
1.7%
772
 
1.7%
672
 
1.7%
872
 
1.7%
570
 
1.6%
Other values (991)3511
81.4%
ValueCountFrequency (%)
198
2.3%
1.21
 
< 0.1%
1.251
 
< 0.1%
1.3333333332
 
< 0.1%
1.59
 
0.2%
1.5454545451
 
< 0.1%
1.5714285711
 
< 0.1%
1.6666666674
 
0.1%
1.8333333331
 
< 0.1%
1.8888888891
 
< 0.1%
ValueCountFrequency (%)
297.88235291
< 0.1%
2591
< 0.1%
2191
< 0.1%
1911
< 0.1%
1711
< 0.1%
1551
< 0.1%
1531
< 0.1%
1482
< 0.1%
1411
< 0.1%
135.33333331
< 0.1%

returns_revenue
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1052
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-89.26887833
Minimum-168469.6
Maximum0
Zeros2824
Zeros (%)65.4%
Negative1491
Negative (%)34.6%
Memory size33.8 KiB
2022-03-02T10:59:18.370771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-168469.6
5-th percentile-140.154
Q1-14.475
median0
Q30
95-th percentile0
Maximum0
Range168469.6
Interquartile range (IQR)14.475

Descriptive statistics

Standard deviation2612.102626
Coefficient of variation (CV)-29.26106695
Kurtosis4006.657749
Mean-89.26887833
Median Absolute Deviation (MAD)0
Skewness-62.33766618
Sum-385195.21
Variance6823080.127
MonotonicityNot monotonic
2022-03-02T10:59:18.891193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02824
65.4%
-12.7520
 
0.5%
-4.9519
 
0.4%
-1517
 
0.4%
-9.9517
 
0.4%
-5.912
 
0.3%
-19.810
 
0.2%
-4.2510
 
0.2%
-25.510
 
0.2%
-3.759
 
0.2%
Other values (1042)1367
31.7%
ValueCountFrequency (%)
-168469.61
< 0.1%
-22998.41
< 0.1%
-14688.241
< 0.1%
-8511.151
< 0.1%
-7443.591
< 0.1%
-5228.41
< 0.1%
-4815.261
< 0.1%
-4814.741
< 0.1%
-4486.241
< 0.1%
-44291
< 0.1%
ValueCountFrequency (%)
02824
65.4%
-0.422
 
< 0.1%
-0.651
 
< 0.1%
-0.951
 
< 0.1%
-1.254
 
0.1%
-1.454
 
0.1%
-1.641
 
< 0.1%
-1.655
 
0.1%
-1.72
 
< 0.1%
-1.791
 
< 0.1%

avg_return_revenue
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1098
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-48.79090548
Minimum-168469.6
Maximum0
Zeros2824
Zeros (%)65.4%
Negative1491
Negative (%)34.6%
Memory size33.8 KiB
2022-03-02T10:59:19.407852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-168469.6
5-th percentile-29.859
Q1-6.564166667
median0
Q30
95-th percentile0
Maximum0
Range168469.6
Interquartile range (IQR)6.564166667

Descriptive statistics

Standard deviation2565.947329
Coefficient of variation (CV)-52.59068885
Kurtosis4305.399441
Mean-48.79090548
Median Absolute Deviation (MAD)0
Skewness-65.58048429
Sum-210532.7572
Variance6584085.695
MonotonicityNot monotonic
2022-03-02T10:59:19.847483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02824
65.4%
-12.7523
 
0.5%
-4.9521
 
0.5%
-9.9520
 
0.5%
-1517
 
0.4%
-4.2510
 
0.2%
-3.7510
 
0.2%
-179
 
0.2%
-7.59
 
0.2%
-8.259
 
0.2%
Other values (1088)1363
31.6%
ValueCountFrequency (%)
-168469.61
< 0.1%
-4599.681
< 0.1%
-1605.0866671
< 0.1%
-1591.21
< 0.1%
-833.251
< 0.1%
-687.821
< 0.1%
-638.61913041
< 0.1%
-5941
< 0.1%
-581.41
< 0.1%
-535.33333331
< 0.1%
ValueCountFrequency (%)
02824
65.4%
-0.422
 
< 0.1%
-0.651
 
< 0.1%
-0.821
 
< 0.1%
-0.951
 
< 0.1%
-1.051
 
< 0.1%
-1.0751
 
< 0.1%
-1.1166666671
 
< 0.1%
-1.255
 
0.1%
-1.381
 
< 0.1%

num_returns
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct57
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.908458864
Minimum0
Maximum223
Zeros2824
Zeros (%)65.4%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-03-02T10:59:20.323432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile9
Maximum223
Range223
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.05963145
Coefficient of variation (CV)3.699126862
Kurtosis303.4869025
Mean1.908458864
Median Absolute Deviation (MAD)0
Skewness13.62081804
Sum8235
Variance49.83839621
MonotonicityNot monotonic
2022-03-02T10:59:20.756239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02824
65.4%
1474
 
11.0%
2283
 
6.6%
3171
 
4.0%
4117
 
2.7%
583
 
1.9%
652
 
1.2%
751
 
1.2%
841
 
1.0%
1122
 
0.5%
Other values (47)197
 
4.6%
ValueCountFrequency (%)
02824
65.4%
1474
 
11.0%
2283
 
6.6%
3171
 
4.0%
4117
 
2.7%
583
 
1.9%
652
 
1.2%
751
 
1.2%
841
 
1.0%
917
 
0.4%
ValueCountFrequency (%)
2231
< 0.1%
1331
< 0.1%
1121
< 0.1%
1111
< 0.1%
921
< 0.1%
901
< 0.1%
811
< 0.1%
781
< 0.1%
701
< 0.1%
621
< 0.1%

qty_returned
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct207
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-43.90382387
Minimum-80995
Maximum0
Zeros2824
Zeros (%)65.4%
Negative1491
Negative (%)34.6%
Memory size33.8 KiB
2022-03-02T10:59:21.206834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-80995
5-th percentile-57
Q1-3
median0
Q30
95-th percentile0
Maximum0
Range80995
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1262.559141
Coefficient of variation (CV)-28.75738443
Kurtosis3921.624481
Mean-43.90382387
Median Absolute Deviation (MAD)0
Skewness-61.38913163
Sum-189445
Variance1594055.584
MonotonicityNot monotonic
2022-03-02T10:59:21.643710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02824
65.4%
-1169
 
3.9%
-2148
 
3.4%
-3105
 
2.4%
-489
 
2.1%
-678
 
1.8%
-561
 
1.4%
-1251
 
1.2%
-744
 
1.0%
-843
 
1.0%
Other values (197)703
 
16.3%
ValueCountFrequency (%)
-809951
< 0.1%
-93601
< 0.1%
-90141
< 0.1%
-80041
< 0.1%
-44271
< 0.1%
-37681
< 0.1%
-33321
< 0.1%
-28781
< 0.1%
-20221
< 0.1%
-20121
< 0.1%
ValueCountFrequency (%)
02824
65.4%
-1169
 
3.9%
-2148
 
3.4%
-3105
 
2.4%
-489
 
2.1%
-561
 
1.4%
-678
 
1.8%
-744
 
1.0%
-843
 
1.0%
-941
 
1.0%

Interactions

2022-03-02T10:58:58.416146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:40.624148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:46.944413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:52.877881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:58.665219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:04.321072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:10.398077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:15.520980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:20.938459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:26.442803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:31.098183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:35.866628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:40.641510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:46.389704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:52.819694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:58.783195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:41.273691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:47.362561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:53.283902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:59.044883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:04.680959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:10.691807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:15.835864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:21.368057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:26.731334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:31.389446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:36.186758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:40.934845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:46.768724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:53.292782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:59.206482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:41.654737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:47.744885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:53.707404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:59.409320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:05.060516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:10.993424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:16.195535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:21.735027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:27.063503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:31.712133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:36.509593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:41.242061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:47.263486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:53.757177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:59.711652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:42.079422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:48.133212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:54.147896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:59.787505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:05.415765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:11.292902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:16.625418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:22.084122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:27.390193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:32.035612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:36.818929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:41.557267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:47.625978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:54.217811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:59:00.027072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:42.392669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:48.491336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:54.528865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:00.108970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:05.758956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:11.575851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:17.001812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:22.508516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:27.685305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:32.326705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:37.121488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:41.851830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:47.966021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:54.546298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:59:00.476777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:42.824357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:48.895160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:54.884730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:00.449416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:06.254647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:11.882622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:17.498979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:22.939848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:27.984974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:32.697560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:37.461668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:42.181126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:48.348567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:54.877976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:59:00.884521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:43.175596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:49.361462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:55.284204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:00.835564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:06.763159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:12.193567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:17.816126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:23.340419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:28.286366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:33.008289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:37.764650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:42.501873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:48.734942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:55.258647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:59:01.275161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:43.543996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:49.739205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:55.666318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:01.149637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:07.260173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:12.479636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:18.128304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:23.668878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:28.582957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:33.319758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:38.055625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:42.805234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:49.165229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:55.595744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:59:01.617713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:43.906996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:50.158607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:56.013451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:01.488372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:07.683397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:12.792039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:18.431408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:24.300175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:28.881924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:33.617208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:38.349230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:43.127873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:49.515906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:55.933763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:59:01.999683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:44.270148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:50.554981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:56.355333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:01.885587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:08.096186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:13.113154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:18.788967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:24.615255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:29.209840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:33.936779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:38.673047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:43.451392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:50.185092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:56.287449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:59:02.402373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:44.694521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:50.901893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:56.748721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:02.270708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:08.530042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:13.652238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:19.127949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:24.931381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:29.538079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:34.258864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:38.996945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:43.796097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:50.680526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:56.640345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:59:02.758281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:45.350121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:51.308026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:57.171602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:02.637535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:08.975428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:14.063545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:19.551535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:25.233658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:29.845082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:34.570127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:39.421961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:44.132550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:51.106977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:56.987850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:59:03.207346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:45.714003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:51.702706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:57.554470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:03.330519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:09.423188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:14.461819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:19.873708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:25.545417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:30.168450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:34.878982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:39.728726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:44.480578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:51.567881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:57.366253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:59:03.567862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:46.139045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:52.116798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:57.957045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:03.650540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:09.809446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:14.862117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:20.188279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:25.830945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:30.470236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:35.218892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:40.053261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:44.941103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:51.966100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:57.738813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:59:03.931391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:46.486292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:52.503989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:57:58.283298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:03.952430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:10.092477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:15.169995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:20.476233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:26.122245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:30.762877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:35.512773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:40.321924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:45.481586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:52.360223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-02T10:58:58.030841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-02T10:59:22.104771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-02T10:59:22.818370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-02T10:59:23.382183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-02T10:59:23.950796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-02T10:59:04.636190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-02T10:59:05.603264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

customer_idcountryrecencyavg_days_bw_purchasesnum_purchasesdate_rangefrequencyrevenueavg_ticketqty_itemsavg_basket_sizeavg_unique_prodsreturns_revenueavg_return_revenuenum_returnsqty_returned
017850United Kingdom3731.00000034134.0000005288.63155.547941169348.3714298.735294-102.58-6.83866715.0-40.0
113047United Kingdom5752.83333393170.0283913089.10343.233333135584.68750019.000000-143.49-6.23869623.0-35.0
212583France326.500000153710.0404316629.34441.9560004978292.82352915.466667-76.04-25.3466673.0-50.0
313748United Kingdom9692.66666752780.017986948.25189.65000043987.8000005.6000000.000.0000000.00.0
415100United Kingdom33420.0000003400.075000635.10211.700000589.6666671.000000-240.90-80.3000003.0-22.0
515291United Kingdom2626.769231143480.0402304551.51325.1078572073109.1052637.285714-71.79-11.9650006.0-29.0
614688United Kingdom819.263158213660.0573775107.38243.2085713222119.33333315.285714-523.49-16.35906332.0-399.0
717809United Kingdom1739.666667123570.0336135344.85445.4041672016144.0000005.083333-67.06-33.5300002.0-41.0
815311United Kingdom14.191011913730.24396859419.34652.95978037720319.66101725.901099-1348.56-12.040714112.0-474.0
916098United Kingdom8847.66666772860.0244762005.63286.51857161387.5714299.4285710.000.0000000.00.0

Last rows

customer_idcountryrecencyavg_days_bw_purchasesnum_purchasesdate_rangefrequencyrevenueavg_ticketqty_itemsavg_basket_sizeavg_unique_prodsreturns_revenueavg_return_revenuenum_returnsqty_returned
430516000United Kingdom30.0331.00000012393.704131.23333351101703.3333333.00.000.000.00.0
430615195United Kingdom30.0130.3333333861.003861.00000014041404.0000001.00.000.000.00.0
430714087United Kingdom30.0130.333333181.67181.670000250125.00000061.0-12.75-12.751.0-1.0
430814204United Kingdom30.0130.333333161.03161.0300008282.00000036.00.000.000.00.0
430915471United Kingdom30.0130.333333469.48469.480000266266.00000067.00.000.000.00.0
431013436United Kingdom20.0120.500000196.89196.8900007676.00000012.00.000.000.00.0
431115520United Kingdom20.0120.500000343.50343.500000314314.00000018.00.000.000.00.0
431213298United Kingdom20.0120.500000360.00360.0000009696.0000002.00.000.000.00.0
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